{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2020-02-27 01:51:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2020-02-27 01:52:00+00:00</th>\n",
       "      <td>167643.05</td>\n",
       "      <td>178565.38</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-27 01:53:00+00:00</th>\n",
       "      <td>167639.96</td>\n",
       "      <td>178565.38</td>\n",
       "      <td>165000.01</td>\n",
       "      <td>168896.91</td>\n",
       "      <td>357.71302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27 01:54:00+00:00</th>\n",
       "      <td>168423.43</td>\n",
       "      <td>178565.38</td>\n",
       "      <td>165000.01</td>\n",
       "      <td>168765.00</td>\n",
       "      <td>359.22871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27 01:55:01+00:00</th>\n",
       "      <td>168831.25</td>\n",
       "      <td>178565.38</td>\n",
       "      <td>165000.01</td>\n",
       "      <td>168101.24</td>\n",
       "      <td>359.35889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Open        High         Low       Close  \\\n",
       "Date                                                                        \n",
       "2020-02-27 01:51:00+00:00   167761.49   178565.38   165000.01   168764.01   \n",
       "2020-02-27 01:52:00+00:00   167643.05   178565.38   165000.01   168764.01   \n",
       "2020-02-27 01:53:00+00:00   167639.96   178565.38   165000.01   168896.91   \n",
       "2020-02-27 01:54:00+00:00   168423.43   178565.38   165000.01   168765.00   \n",
       "2020-02-27 01:55:01+00:00   168831.25   178565.38   165000.01   168101.24   \n",
       "\n",
       "                               Volume  \n",
       "Date                                   \n",
       "2020-02-27 01:51:00+00:00   358.79390  \n",
       "2020-02-27 01:52:00+00:00   357.62323  \n",
       "2020-02-27 01:53:00+00:00   357.71302  \n",
       "2020-02-27 01:54:00+00:00   359.22871  \n",
       "2020-02-27 01:55:01+00:00   359.35889  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-27 02:05:03+00:00</th>\n",
       "      <td>167962.76</td>\n",
       "      <td>178560.31</td>\n",
       "      <td>165000.01</td>\n",
       "      <td>167833.41</td>\n",
       "      <td>358.46845</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-27 02:06:04+00:00</th>\n",
       "      <td>167962.76</td>\n",
       "      <td>178560.31</td>\n",
       "      <td>165000.01</td>\n",
       "      <td>167891.95</td>\n",
       "      <td>358.38835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27 02:07:03+00:00</th>\n",
       "      <td>167977.55</td>\n",
       "      <td>178560.31</td>\n",
       "      <td>165000.01</td>\n",
       "      <td>167805.38</td>\n",
       "      <td>358.37288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27 02:08:04+00:00</th>\n",
       "      <td>168063.80</td>\n",
       "      <td>178198.74</td>\n",
       "      <td>165000.01</td>\n",
       "      <td>167806.28</td>\n",
       "      <td>357.97818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27 02:09:04+00:00</th>\n",
       "      <td>167833.41</td>\n",
       "      <td>178198.74</td>\n",
       "      <td>165000.01</td>\n",
       "      <td>168980.93</td>\n",
       "      <td>357.86681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Open        High         Low       Close  \\\n",
       "Date                                                                        \n",
       "2020-02-27 02:05:03+00:00   167962.76   178560.31   165000.01   167833.41   \n",
       "2020-02-27 02:06:04+00:00   167962.76   178560.31   165000.01   167891.95   \n",
       "2020-02-27 02:07:03+00:00   167977.55   178560.31   165000.01   167805.38   \n",
       "2020-02-27 02:08:04+00:00   168063.80   178198.74   165000.01   167806.28   \n",
       "2020-02-27 02:09:04+00:00   167833.41   178198.74   165000.01   168980.93   \n",
       "\n",
       "                               Volume  \n",
       "Date                                   \n",
       "2020-02-27 02:05:03+00:00   358.46845  \n",
       "2020-02-27 02:06:04+00:00   358.38835  \n",
       "2020-02-27 02:07:03+00:00   358.37288  \n",
       "2020-02-27 02:08:04+00:00   357.97818  \n",
       "2020-02-27 02:09:04+00:00   357.86681  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('stackoverflow.questions.60982755.csv',index_col=0,parse_dates=True)\n",
    "\n",
    "df.index.name = 'Date'\n",
    "df.shape\n",
    "df.head()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Open', 'High', 'Low', 'Close', 'Volume'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = [col.strip() for col in df.columns]\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.12.4a0'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mplfinance as mpf\n",
    "mpf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x414 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(df,type='candle',volume=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>168190.463684</td>\n",
       "      <td>178524.918421</td>\n",
       "      <td>1.650000e+05</td>\n",
       "      <td>168191.451579</td>\n",
       "      <td>358.806738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>431.612733</td>\n",
       "      <td>114.969577</td>\n",
       "      <td>8.970403e-11</td>\n",
       "      <td>424.192030</td>\n",
       "      <td>0.725471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>167630.310000</td>\n",
       "      <td>178198.740000</td>\n",
       "      <td>1.650000e+05</td>\n",
       "      <td>167630.310000</td>\n",
       "      <td>357.623230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>168896.910000</td>\n",
       "      <td>178565.380000</td>\n",
       "      <td>1.650000e+05</td>\n",
       "      <td>168980.930000</td>\n",
       "      <td>359.941400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open           High           Low          Close      Volume\n",
       "mean  168190.463684  178524.918421  1.650000e+05  168191.451579  358.806738\n",
       "std      431.612733     114.969577  8.970403e-11     424.192030    0.725471\n",
       "min   167630.310000  178198.740000  1.650000e+05  167630.310000  357.623230\n",
       "max   168896.910000  178565.380000  1.650000e+05  168980.930000  359.941400"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().loc[['mean','std','min','max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
